Potential DNA Barcoding for Identification of Large Leaf Homalomena (Homalomena pendula (Blume) Bakh.f.)
Bibliographic record
Abstract
Background: Homalomena is a genus of the Araceae family, which consists of many medicinal plants used in Vietnamese traditional medicine. However, this genus shows relatively similar morphological characteristics across its species, so classification by morphological comparison has several limitations, and the scientific names and classifications of some species remain controversial. H. pendula is an endangered medicinal herb, highly valued for its tonic, analgesic, and anti-inflammatory properties, but wild populations are threatened by overharvesting. In addition, the DNA barcodes for H. pendula have not been well studied. Thus, developing DNA barcodes for H. pendula is necessary to identify and conserve this species.Methods: This study used four DNA barcodes (trnL-trnF, rbcL, trnH-psbA, and trnQ-rps16) to determine the most effective DNA barcode sequences for distinguishing H. pendula.Results: Among 4 Homalomena species, one SNP was found in the trnL-trnF sequence at position 16 (A > G), and three SNPs were found in the rbcL sequence at positions 126 (C > T), 272 (G > A), and 278 (T > A). Five different H. pendula samples collected from Hue city, Vietnam, distributed in a separate branch from trnL-trnF or rbcL on the phylogenetic tree. Both trnH-psbA and trnQ-rps16 fragments have lots of changes in nucleotide sequences, however, these are random differences and are not significant in H. pendula identification, only useful for genetic diversity studies.Conclusion: Based on the nucleotide sequences and phylogenetic tree analysis, the results show that trnL-trnF and rbcL markers can be used to distinguish H. pendula from other closely related species.Keywords: Chloroplast, DNA Barcode, Homalomena pendula, Phylogeny, Natural Forest, Vietnam
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".